CAD-model-based vision for space applications

A pose acquisition system operating in space must be able to perform well in a variety of different applications including automated guidance and inspections tasks with many different, but known objects. Since the space station is being designed with automation in mind, there will be CAD models of all the objects, including the station itself. The construction of vision models and procedures directly from the CAD models is the goal of this project. The system that is being designed and implementing must convert CAD models to vision models, predict visible features from a given view point from the vision models, construct view classes representing views of the objects, and use the view class model thus derived to rapidly determine the pose of the object from single images and/or stereo pairs

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.Comment: 11 PAGE

Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy

Autonomous vehicles are becoming central for the future of mobility, supported by advances in deep learning techniques. The performance of aself-driving system is highly dependent on the quality of the perception task. Developments in sensor technologies have led to an increased availability of 3D scanners such as LiDAR, allowing for a more accurate representation of the vehicle's surroundings, leading to safer systems. The rapid development and consequent rise of research studies around self-driving systems since early 2010, resulted in a tremendous increase in the number and novelty of object detection methods. After the first wave of works that essentially tried to expand known techniques from object detection in images, more recently there has been a notable development in newer and more adapted to LiDAR data works. This paper addresses the existing literature on object detection using LiDAR data within the scope of self-driving and brings a systematic way for analysing it. Unlike general object detection surveys, we will focus on point-cloud data, which presents specific challenges, notably its high-dimensional and sparse nature. This work introduces a common object detection pipeline and taxonomy to facilitate a thorough comparison between different techniques and, departing from it, this work will critically examine the representation of data (critical for complexity reduction), feature extraction and finally the object detection models. A comparison between performance results of the different models is included, alongside with some future research challenges.This work is supported by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n. 037902; Funding Reference: POCI-01-0247-FEDER-037902]

Investigation on soft computing techniques for airport environment evaluation systems

Spatial and temporal information exist widely in engineering fields, especially in airport environmental management systems. Airport environment is influenced by many different factors and uncertainty is a significant part of the system. Decision support considering this kind of spatial and temporal information and uncertainty is crucial for airport environment related engineering planning and operation. Geographical information systems and computer aided design are two powerful tools in supporting spatial and temporal information systems. However, the present geographical information systems and computer aided design software are still too general in considering the special features in airport environment, especially for uncertainty. In this thesis, a series of parameters and methods for neural network-based knowledge discovery and training improvement are put forward, such as the relative strength of effect, dynamic state space search strategy and compound architecture. [Continues.

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 333)

This bibliography lists 122 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during January, 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

COVID-19 Outbreak Prediction with Machine Learning

Abstract: Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and these models are popular in the media. Due to a high Level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models need to be improved. This paper presents a comparative analysis ofmachine learning and soft computingmodels to predict the COVID-19 outbreak as an alternative to susceptible–infected–recovered (SIR) and susceptible-exposed-infectious-removed (SEIR) models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP; and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior across nations, this study suggests machine Learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. This paper further suggests that a genuine novelty in outbreak prediction can be realized by integrating machine learning and SEIR models.publishedVersio

Video-Based Environment Perception for Automated Driving using Deep Neural Networks

Automatisierte Fahrzeuge benötigen eine hochgenaue Umfeldwahrnehmung, um sicher und komfortabel zu fahren. Gleichzeitig müssen die Perzeptionsalgorithmen mit der verfügbaren Rechenleistung die Echtzeitanforderungen der Anwendung erfüllen. Kamerabilder stellen eine sehr wichtige Informationsquelle für automatisierte Fahrzeuge dar. Sie beinhalten mehr Details als Daten von anderen Sensoren wie Lidar oder Radar und sind oft vergleichsweise günstig. Damit ist es möglich, ein automatisiertes Fahrzeug mit einem Surround-View Sensor-Setup auszustatten, ohne die Gesamtkosten zu stark zu erhöhen. In dieser Arbeit präsentieren wir einen effizienten und genauen Ansatz zur videobasierten Umfeldwahrnehmung für automatisierte Fahrzeuge. Er basiert auf Deep Learning und löst die Probleme der Objekterkennung, Objektverfolgung und der semantischen Segmentierung von Kamerabildern. Wir schlagen zunächst eine schnelle CNN-Architektur zur gleichzeitigen Objekterkennung und semantischen Segmentierung vor. Diese Architektur ist skalierbar, so dass Genauigkeit leicht gegen Rechenzeit eingetauscht werden kann, indem ein einziger Skalierungsfaktor geändert wird. Wir modifizieren diese Architektur daraufhin, um Embedding-Vektoren für jedes erkannte Objekt vorherzusagen. Diese Embedding-Vektoren werden als Assoziationsmetrik bei der Objektverfolgung eingesetzt. Sie werden auch für einen neuartigen Algorithmus zur Non-Maximum Suppression eingesetzt, den wir FeatureNMS nennen. FeatureNMS kann in belebten Szenen, in denen die Annahmen des klassischen NMS-Algorithmus nicht zutreffen, einen höheren Recall erzielen. Wir erweitern anschlie{\ss}end unsere CNN-Architektur für Einzelbilder zu einer Mehrbild-Architektur, welche zwei aufeinanderfolgende Videobilder als Eingabe entgegen nimmt. Die Mehrbild-Architektur schätzt den optischen Fluss zwischen beiden Videobildern innerhalb des künstlichen neuronalen Netzwerks. Dies ermöglicht es, einen Verschiebungsvektor zwischen den Videobildern für jedes detektierte Objekt zu schätzen. Diese Verschiebungsvektoren werden ebenfalls als Assoziationsmetrik bei der Objektverfolgung eingesetzt. Zuletzt präsentieren wir einen einfachen Tracking-by-Detection-Ansatz, der wenig Rechenleistung erfordert. Er benötigt einen starken Objektdetektor und stützt sich auf die Embedding- und Verschiebungsvektoren, die von unserer CNN-Architektur geschätzt werden. Der hohe Recall des Objektdetektors führt zu einer häufigen Detektion der verfolgten Objekte. Unsere diskriminativen Assoziationsmetriken, die auf den Embedding- und Verschiebungsvektoren basieren, ermöglichen eine zuverlässige Zuordnung von neuen Detektionen zu bestehenden Tracks. Diese beiden Bestandteile erlauben es, ein einfaches Bewegungsmodell mit Annahme einer konstanten Geschwindigkeit und einem Kalman-Filter zu verwenden. Die von uns vorgestellten Methoden zur videobasierten Umfeldwahrnehmung erreichen gute Resultate auf den herausfordernden Cityscapes- und BDD100K-Datensätzen. Gleichzeitig sind sie recheneffizient und können die Echtzeitanforderungen der Anwendung erfüllen. Wir verwenden die vorgeschlagene Architektur erfolgreich innerhalb des Wahrnehmungs-Moduls eines automatisierten Versuchsfahrzeugs. Hier hat sie sich in der Praxis bewähren können